Trees and Forests in Nuclear Physics
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We present a detailed introduction to the decision tree algorithm using some simple examples taken from the domain of nuclear physics. We show how to improve the accuracy of the classical liquid drop nuclear mass model by performing Feature Engineering while using a decision tree. Finally, we apply the method to the Duflo-Zucker mass model showing that, despite their simplicity, decision trees are capable of obtaining a level of accuracy comparable to more complex neural networks, but using way less adjustable parameters and obtaining easier to explain models.
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